@Article{Ammar:2016:Neurocomputing,
author = "Marwa Ammar and Souhir Bouaziz and Adel M. Alimi and
Ajith Abraham",
title = "Multi-agent architecture for Multiaobjective
optimization of Flexible Neural Tree",
journal = "Neurocomputing",
volume = "214",
pages = "307--316",
year = "2016",
ISSN = "0925-2312",
DOI = "doi:10.1016/j.neucom.2016.06.019",
URL = "http://www.sciencedirect.com/science/article/pii/S0925231216306579",
abstract = "In this paper, a multi-agent system is introduced to
parallelize the Flexible Beta Basis Function Neural
Network (FBBFNT)' training as a response to the time
cost challenge. Different agents are formed; a
Structure Agent is designed for the FBBFNT structure
optimization and a variable set of Parameter Agents is
used for the FBBFNT parameter optimization. The main
objectives of the FBBFNT learning process were the
accuracy and the structure complexity. With the
proposed multi-agent system, the main purpose is to
reach a good balance between these objectives. For
that, a multi-objective context was adopted which based
on Pareto dominance. The agents use two algorithms: the
Pareto dominance Extended Genetic Programming (PEGP)
and the Pareto Multi-Dimensional Particle Swarm
Optimization ( PMD _ PSO ) algorithms for the structure
and parameter optimization, respectively. The proposed
system is called Pareto Multi-Agent Flexible Neural
Tree ( PMA _ FNT ). To assess the effectiveness of PMA
_ FNT , four benchmark real datasets of classification
are tested. The results compared with some classifiers
published in the literature.",
keywords = "genetic algorithms, genetic programming, Flexible
Neural Tree, Multi-agent architecture, Multi-objective
optimization, Evolutionary Computation algorithms,
Negotiation, Classification",
}